Senior MLOps Engineer

Job Summary

We are seeking a highly experienced MLOps Engineer to design, implement, and maintain robust machine learning operations infrastructure. The ideal candidate will have deep expertise in building scalable ML pipelines, automating model deployment workflows, and integrating CI/CD practices to support data science and AI initiatives across the enterprise.

Key Responsibilities

• Design and implement end-to-end ML pipelines for data ingestion, model training, validation, deployment, and monitoring.
• Automate ML workflows using CI/CD tools and infrastructure-as-code practices.
• Collaborate with data scientists, engineers, and DevOps teams to streamline experimentation and productionization of models.
• Manage model versioning, reproducibility, and performance tracking across environments.
• Integrate ML systems with cloud platforms (AWS, Azure, GCP) and container orchestration tools (Docker, Kubernetes).
• Monitor deployed models for drift, latency, and reliability, and implement retraining strategies.
• Ensure security, scalability, and compliance of ML infrastructure.
• Document architecture, workflows, and operational procedures.

Required Skills & Qualifications

• Minimum 5 years of experience in MLOps, DevOps, or machine learning engineering roles.
• Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Hands-on experience with ML pipeline tools (e.g., MLflow, Kubeflow, Airflow).
• Expertise in CI/CD tools (e.g., Jenkins, GitHub Actions, GitLab CI).
• Familiarity with cloud services and infrastructure automation (Terraform, CloudFormation).
• Solid understanding of containerization (Docker) and orchestration (Kubernetes).
• Excellent problem-solving, communication, and collaboration skills.

Job Type: Full-time

Work Location: Remote